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Learning Dynamics of Biological Processes from Time Course Omics Datasets

从时间过程组学数据集中学习生物过程的动力学

基本信息

批准号：
10473720
负责人：
Sumanta Basu
金额：
$ 35.74万
依托单位：
CORNELL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-23 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10473720
关键词：
Address Algorithms Architecture Basic Science Behavior Biological Biological Process Clinical Collection Complex Computer software Data Data Analyses Data Set Decision Trees Development Developmental Process Dimensions Disease Progression Drosophila genus Etiology Gene Proteins Genes Grouping Immune System Diseases Immune response Innate Immune Response Knowledge Lead Learning Methodology Methods Modeling Monitor Nature Pathway Analysis Pathway interactions Patients Pattern Process Research Research Project Grants Role Sampling Scientist Series Silicon Dioxide System Techniques Time Uncertainty Validation Variant base biological systems clinical practice disease prognosis dynamic system experimental study high dimensionality innovation insight learning algorithm learning strategy mouse model novel open source organ growth protein protein interaction random forest temporal measurement

项目摘要

Complex biological processes, including organ development, immune response and disease progression, are inherently dynamic. Learning their regulatory architecture requires understanding how components of a large system dynamically interact with each other and give rise to emergent behavior. Recent experimental advances have made ii possible to investigate these biological systems in a data-driven fashion al high temporal resolution, allowing identification of new genes and their regulatory interactions. Longitudinal omics data sets are becoming increasingly common in clinical practice as well. Information on these collections of interacting genes can be integrated to gain systems-level insights into the roles of biological pathways and processes, including progression of diseases. Consequently, developing interpretable methods for learning functional relationships among genes, proteins or metabolites from high-dimensional time series data has become a timely research problem. The nature of these time-course data sets presents exciting opportunities and interesting challenges from a statistical perspective. Typical time-course omics data sets are challenging because of their high-dimensionality and non-linear relationships among system components. To tackle these challenges, one needs sophisticated dimension-reduction techniques that are biologically meaningful, computationally efficient and allow uncertainty quantification. Methods that incorporate prior biological information (e.g., pathway membership, protein-protein interactions) into the data analysis are good candidates for analyzing such high-dimensional systems using small samples. Here, we will develop three core methods to address the above challenges - (Aim 1): an empirical Bayes framework for clustering high-dimensional omics time-course data using prior biological knowledge; (Aim 2): a quantile-based Granger causality framework for learning interactions among genes or metabolites from their lead-lag relationships; and (Aim 3): a decision tree ensemble framework for searching cascades of interactions among genes from their temporal expression profiles. Our interdisciplinary team of statisticians and scientists will analyze time-course omics data from three research projects: (i) innate immune response systems in Drosophila, (ii) developmental process in mouse models, and (ii) longitudinal metabolite profiling of TB patients. These insights will be used to build and validate our methodology, which will be implemented in a publicly available software. This proposal is innovative in its incorporation of prior biological knowledge in the framework of novel dimension reduction techniques for interrogating high-dimensional time-course omics data. This research is significant in that it will impact basic sciences by elucidating data-driven, testable hypotheses on the regulatory architecture of biological processes, and clinical practice by monitoring disease progression and prognosis.

复杂的生物过程，包括器官发育、免疫反应和疾病进展，从本质上讲是动态的。了解他们的监管架构需要了解如何大系统的组件之间动态地相互作用，并产生紧急行为。最近的实验进展使研究这些生物系统成为可能。数据驱动的时尚和高时间分辨率，允许识别新的基因及其调控互动。纵向组学数据集在临床实践中变得越来越常见也是。关于这些相互作用的基因集合的信息可以被整合以获得系统级的洞察生物途径和过程的作用，包括疾病的进展。因此，开发可解释的方法来学习基因、蛋白质之间的功能关系或从高维时间序列数据中提取代谢物已成为一个及时的研究问题。这些时间进程数据集的性质带来了令人兴奋的机遇和有趣的挑战从统计学的角度来看。典型的时间进程组学数据集具有挑战性，因为它们的高维性和系统组件之间的非线性关系。为了应对这些挑战，人们需要复杂的降维技术，这些技术在生物学上、计算上都是有意义的效率高，并允许不确定量化。结合先前生物学的方法进入数据分析的信息(例如，通路成员、蛋白质-蛋白质相互作用)是好的使用小样本分析这种高维系统的候选对象。在这里，我们将开发三个核心方法来应对上述挑战-(目标1)：经验性使用先验生物学知识对高维组学时间进程数据进行聚类的贝叶斯框架 (目标2)：基于分位数的Granger因果关系框架用于学习基因之间的相互作用或来自其领先-滞后关系的代谢物；以及(目标3)：决策树集成框架，用于从基因的时间表达谱中搜索基因之间相互作用的级联。我们的跨学科一组统计学家和科学家将分析来自三项研究的时间进程组学数据项目：(I)果蝇的先天免疫反应系统，(Ii)小鼠模型的发育过程，和(Ii)结核病患者的纵向代谢物图谱。这些见解将被用来建立和验证我们的方法，这将在一个公开可用的软件中实现。这项建议是创新性地将先前的生物学知识纳入到新的降维框架中用于询问高维时间进程组学数据的技术。这项研究具有重要的意义它将通过阐明关于监管架构的数据驱动的、可测试的假设来影响基础科学通过监测疾病的进展和预后，对生物过程和临床实践进行研究。